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personalized_passkey_retrieval.py
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""personalized_passkey_retrieval: a synthetic dataset to evaluate long-context embeddings"""
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import json
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import gzip
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import datasets
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logger = datasets.logging.get_logger(__name__)
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@inproceedings{Wang2023ImprovingTE,
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title={Improving Text Embeddings with Large Language Models},
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author={Liang Wang and Nan Yang and Xiaolong Huang and Linjun Yang and Rangan Majumder and Furu Wei},
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year={2023},
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}
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"""
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# You can copy an official description
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_DESCRIPTION = """\
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This dataset contains synthetic data for personalized passkey retrieval.
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It is only intended for evaluation purposes, you should not use it for training.
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"""
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_URLS = {
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"train": "personalized_passkey_retrieval.jsonl.gz"
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}
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class Query2docMsmarco(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("0.1.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name='plain_text', version=VERSION, description='plain text')
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]
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def _info(self):
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features = datasets.Features(
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{
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"query": datasets.Value("string"),
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"candidates": datasets.features.Sequence(datasets.Value("string")),
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"label": datasets.Value("int32"),
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"context_length": datasets.Value("int32"),
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download(_URLS)
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print(downloaded_files)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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"filepath": downloaded_files["train"],
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"split": "train",
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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id_ = 0
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with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
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for line in f:
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if line:
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example = json.loads(line)
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yield id_, {
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"query": example["query"],
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"candidates": example["candidates"],
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"label": example["label"],
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"context_length": example["context_length"],
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}
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id_ += 1
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personalized_passkey_retrieval.jsonl.gz → train.jsonl.gz
RENAMED
File without changes
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